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lupuletic

onyx-mcp-server

by lupuletic
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TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation4/5

    The two tools have distinct purposes: one is for interactive chat to get answers, and the other is for searching documents. While both involve querying the Onyx backend, the descriptions clarify that 'chat_with_onyx' provides comprehensive answers through conversation, whereas 'search_onyx' focuses on retrieving relevant documents, reducing ambiguity. However, an agent might still confuse them if the distinction between 'answers' and 'documents' is not clear in practice.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with 'chat_with_onyx' and 'search_onyx', using snake_case throughout. The naming is predictable and readable, with no deviations or mixed conventions, making it easy for agents to understand the action and target.

    Tool Count2/5

    With only 2 tools, the server feels thin for a general-purpose 'onyx-mcp-server', as it likely covers a limited scope of interaction with the Onyx backend. This minimal set may not support complex workflows or comprehensive operations, suggesting an under-scoped tool surface that could hinder agent capabilities.

    Completeness2/5

    Inferring the domain as interacting with the Onyx backend, the tool set has significant gaps. It lacks CRUD operations (e.g., create, update, delete documents), management functions, or advanced querying beyond basic search and chat. This incomplete coverage will likely cause agent failures when tasks require more than simple retrieval or conversation.

  • Average 2.8/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • This repository includes a glama.json configuration file.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'comprehensive answers' but fails to describe key traits such as whether this is a read-only operation, if it requires authentication, rate limits, or how chat sessions are managed. This leaves significant gaps in understanding the tool's behavior.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, efficient sentence with no wasted words. It is appropriately sized and front-loaded, clearly stating the tool's core function without unnecessary elaboration.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the complexity of a chat tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It does not explain return values, error handling, or how the tool integrates with the sibling 'search_onyx', leaving the agent with insufficient context for effective use.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has 100% description coverage, so the schema fully documents all 5 parameters. The description adds no additional meaning beyond what the schema provides, such as explaining how parameters interact or their practical use. This meets the baseline for high schema coverage.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the tool's purpose as 'Chat with Onyx to get comprehensive answers', which identifies the action (chat) and resource (Onyx) but is vague about what distinguishes it from the sibling tool 'search_onyx'. It lacks specificity on how chatting differs from searching, leaving the purpose unclear in context.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance is provided on when to use this tool versus the sibling 'search_onyx'. The description does not mention alternatives, exclusions, or contextual usage, leaving the agent without direction on tool selection.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions searching for 'relevant documents' but doesn't describe what constitutes relevance, how results are ranked, whether there are rate limits, authentication requirements, or what the output format looks like. For a search tool with 6 parameters and no annotation coverage, this is insufficient.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a tool with a clear primary function and is front-loaded with the essential information.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the complexity (6 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain what 'relevant' means, how results are returned, or provide any behavioral context. For a search tool that likely returns structured data, more completeness is needed to help an agent use it effectively.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema description coverage is 100%, meaning all parameters are documented in the schema itself. The description adds no additional parameter semantics beyond what's already in the schema (e.g., it doesn't explain how 'chunksAbove' and 'chunksBelow' work together or what 'documentSets' represent). Baseline 3 is appropriate when the schema does the heavy lifting.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Search') and target ('the Onyx backend for relevant documents'), providing a specific verb+resource combination. However, it doesn't differentiate from its sibling tool 'chat_with_onyx', which appears to be a related but distinct functionality, so it doesn't fully distinguish from alternatives.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus the sibling 'chat_with_onyx' or any other alternatives. It lacks context about appropriate use cases, exclusions, or prerequisites, offering only a basic functional statement without usage direction.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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